Mental Health Service Use Among Children with Chronic Physical Illness
Notice bibliographique
Résumé
Background: Children with chronic physical illness (e.g., diabetes, epilepsy) are significantly more likely to experience adverse mental health. While mental health service use (MHSU) amongst Canadian children has increased dramatically in the past two decades, the extent of service use among those with co-occurring physical and mental illness (i.e., multimorbidity) is relatively unknown. The cross-sectional design of previous research limits our understanding of how MHSU for children with chronic physical illness changes over time and the factors that predict differences in patterns of use. This study used longitudinal data to overcome limitations of previous work to better understand MHSU among children with chronic physical illness. \n \nObjectives: This study described the frequency and patterns of MHSU among children and youth (herein children) with chronic physical illness, examined how patterns of MHSU for these children change over a 24-month period, and identified sociodemographic and health-related factors associated with patterns of MHSU among children with chronic physical illness. \n \nMethods: Data come from a sample of 263 children aged 2-16 years who were diagnosed with a chronic physical illness from McMaster Children’s Hospital. Univariate statistics described the mental health services used by children with multimorbidity. Latent class analysis was used to identify patterns of use (e.g., primarily hospital-based vs. community-based). Multinomial regression was used to model baseline sociodemographic and health factors associated with different patterns of MHSU. \n \nResults: Across all timepoints, approximately one quarter of parents reported that their child had some form of contact with a health professional for their mental health (24.7%). Latent class analysis determined a two-class model with one class reporting any contact for their mental health (11.4% at baseline; 16.4% at 24 months) while the other class reported no service use regarding their mental health (88.6% at baseline; 83.7% at 24 months). In a fully-adjusted model, child age (OR = 1.30 [1.15, 1.46]), presence of one or more mental illness (OR = 5.58 [2.19, 14.18]), level of disability (OR = 1.09 [1.02, 1.17]), and parental educational attainment (OR = 3.12 [1.56, 6.26]) differed significantly between classes. \n \nConclusion: Findings suggest that mental health service needs are pervasive in this group of children given both the proportion as well as the array of combinations of health care providers reported by children and their families. Latent class analysis showed a two-class solution that differed with regards to several sociodemographic and health-related factors between those who did and did not report service use. Future directives are required to parse the complex interactions children and their families must navigate. Larger, more diverse samples should be studied in order to replicate findings, and data linkages to health records should be undertaken in effort to mitigate the potential limitations of parent-reported service use.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».